CC BY-NC-ND 4.0 · Appl Clin Inform 2020; 11(05): 769-784
DOI: 10.1055/s-0040-1718755
Research Article

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

Ipek Ensari
1   Data Science Institute, Columbia University, New York, New York, United States
,
Adrienne Pichon
2   Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
,
Sharon Lipsky-Gorman
2   Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
,
Suzanne Bakken
2   Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
3   Columbia School of Nursing, Columbia University, New York, New York, United States
,
Noémie Elhadad
2   Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
› Institutsangaben
Funding This study received financial support from Columbia University Data Science Institute Postdoctoral Fellowship, Endometriosis Foundation of America, National Science Foundation (grant number: 1344668), and from National Institutes of Health, U.S. National Library of Medicine (grant number: R01 LM013043).

Abstract

Background Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

Objectives This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

Methods This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

Results Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

Conclusion For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Protection of Human and Animal Subjects

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.


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Supplementary Material



Publikationsverlauf

Eingereicht: 17. Mai 2020

Angenommen: 14. Juli 2020

Artikel online veröffentlicht:
18. November 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Institute of Medicine (US), Roundtable on Evidence-Based Medicine. Olsen L, Aisner D, McGinnis JM. eds. The Learning Healthcare System: Workshop Summary. Washington, DC: National Academies Press; 2007
  • 2 McGinnis JM, Powers B, Grossmann C. Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Washington, DC: National Academies Press; 2011
  • 3 Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010; 2 (57) 57cm29
  • 4 Riley WT, Glasgow RE, Etheredge L, Abernethy AP. Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise. Clin Transl Med 2013; 2 (01) 10
  • 5 Greene SM, Reid RJ, Larson EB. Implementing the learning health system: from concept to action. Ann Intern Med 2012; 157 (03) 207-210
  • 6 Green LW. Making research relevant: if it is an evidence-based practice, where's the practice-based evidence?. Fam Pract 2008; 25 (Suppl. 01) i20-i24
  • 7 Cortez A, Hsii P, Mitchell E, Riehl V, Smith P. Conceptualizing a data infrastructure for the capture, use, and sharing of patient-generated health data in care delivery and research through 2024. Accenture. . Available at: https://www.healthit.gov/sites/default/files/onc_pghd_practical_guide.pdf?platform=hootsuite. Accessed January, 2018
  • 8 Casey JA, Schwartz BS, Stewart WF, Adler NE. Using electronic health records for population health research: a review of methods and applications. Annu Rev Public Health 2016; 37: 61-81
  • 9 Lin KJ, Singer DE, Glynn RJ, Murphy SN, Lii J, Schneeweiss S. Identifying patients with high data completeness to improve validity of comparative effectiveness research in electronic health records data. Clin Pharmacol Ther 2018; 103 (05) 899-905
  • 10 Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012; 13 (06) 395-405
  • 11 Acker B, Bronnert J, Brown T. et al. Problem list guidance in the EHR. J AHIMA 2011; 82 (09) 52-58
  • 12 Blondeau C. Pocket Glossary of Health Information Management and Technology. 3rd Edition. Chicago, IL: AHIMA; 2011
  • 13 Daskivich TJ, Abedi G, Kaplan SH. et al. Electronic health record problem lists: accurate enough for risk adjustment?. Am J Manag Care 2018; 24 (01) e24-e29
  • 14 Wang M, Cyhaniuk A, Cooper DL, Iyer NN. Identification of patients with congenital hemophilia in a large electronic health record database. J Blood Med 2017; 8: 131-139
  • 15 Wang YC, Shimbo D, Muntner P, Moran AE, Krakoff LR, Schwartz JE. Prevalence of masked hypertension among US adults with nonelevated clinic blood pressure. Am J Epidemiol 2017; 185 (03) 194-202
  • 16 Wang EC-H, Wright A. Characterizing outpatient problem list completeness and duplications in the electronic health record. J Am Med Inform Assoc 2020; 27 (08) 1190-1197
  • 17 Ommaya AK, Cipriano PF, Hoyt DB. et al. Care-centered clinical documentation in the digital environment: solutions to alleviate burnout. NAM Perspectives. . Available at: https://nam.edu/wp-content/uploads/2018/01/Care-Centered-Clinical-Documentation.pdf. Accessed January 29, 2018
  • 18 Collier R. Electronic health records contributing to physician burnout. Can Med Assoc. Available at: https://europepmc.org/article/med/29133547. Accessed 2017
  • 19 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-114
  • 20 West SL, Johnson W, Visscher W, Kluckman M, Qin Y, Larsen A. The challenges of linking health insurer claims with electronic medical records. Health Informatics J 2014; 20 (01) 22-34
  • 21 Adane K, Gizachew M, Kendie S. The role of medical data in efficient patient care delivery: a review. Risk Manag Healthc Policy 2019; 12: 67-73
  • 22 Rhodes ET, Laffel LM, Gonzalez TV, Ludwig DS. Accuracy of administrative coding for type 2 diabetes in children, adolescents, and young adults. Diabetes Care 2007; 30 (01) 141-143
  • 23 Valikodath NG, Newman-Casey PA, Lee PP, Musch DC, Niziol LM, Woodward MA. Agreement of ocular symptom reporting between patient-reported outcomes and medical records. JAMA Ophthalmol 2017; 135 (03) 225-231
  • 24 Cahill S, Makadon H. Sexual orientation and gender identity data collection in clinical settings and in electronic health records: a key to ending LGBT health disparities. LGBT Health 2014; 1 (01) 34-41
  • 25 Bagley SC, Altman RB. Computing disease incidence, prevalence and comorbidity from electronic medical records. J Biomed Inform 2016; 63: 108-111
  • 26 Almalki M, Gray K, Sanchez FM. The use of self-quantification systems for personal health information: big data management activities and prospects. Health Inf Sci Syst 2015; 3 (Suppl. 01) S1
  • 27 Swan M. Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int J Environ Res Public Health 2009; 6 (02) 492-525
  • 28 Khovanskaya V, Baumer EPS, Cosley D, Voida S, Gay G. Everybody knows what you're doing: a critical design approach to personal informatics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Available at: https://stephen.voida.com/uploads/Publications/Publications/khovanskaya-chi13.pdf. Accessed 2013
  • 29 Figueiredo M, Caldeira C, Chen Y, Zheng K. Routine self-tracking of health: reasons, facilitating factors, and the potential impact on health management practices. AMIA Annu Symp Proc 2018; 2017: 706-714
  • 30 Shapiro M, Johnston D, Wald J, Mon D. Patient-generated health data. RTI International. . Available at: https://www.healthit.gov/sites/default/files/rti_pghd_whitepaper_april_2012.pdfApril. Accessed April 2012
  • 31 Boyer GS, Templin DW, Goring WP. et al. Discrepancies between patient recall and the medical record. Potential impact on diagnosis and clinical assessment of chronic disease. Arch Intern Med 1995; 155 (17) 1868-1872
  • 32 Sampson UKA, Kaplan RM, Cooper RS. et al. Reducing health inequities in the U.S.: recommendations from the NHLBI's health inequities think tank meeting. J Am Coll Cardiol 2016; 68 (05) 517-524
  • 33 Ahmed S, Berzon RA, Revicki DA. International Society for Quality of Life Research. et al; The use of patient-reported outcomes (PRO) within comparative effectiveness research: implications for clinical practice and health care policy. Med Care 2012; 50 (12) 1060-1070
  • 34 Pichon A, Schiffer K, Horran E, Bakken S, Mamykina L, Elhadad N. Divided We Stand: The Collaborative Work of Patients and Providers in an Enigmatic Chronic Disease. [under anonymous review]. . Available at: http://www.columbia.edu/∼ab3886/docs/APichon_CV_Jan2020.pdf. Accessed 2020
  • 35 Payne TH, Corley S, Cullen TA. et al. Report of the AMIA EHR-2020 task force on the status and future direction of EHRs. J Am Med Inform Assoc 2015; 22 (05) 1102-1110
  • 36 Rycroft-Malone J, Seers K, Titchen A, Harvey G, Kitson A, McCormack B. What counts as evidence in evidence-based practice?. J Adv Nurs 2004; 47 (01) 81-90
  • 37 Dullabh P, Hovey L, Heaney-Huls K, Rajendran N, Wright A, Sittig DF. Application programming interfaces in health care: findings from a current-state sociotechnical assessment. Appl Clin Inform 2020; 11 (01) 59-69
  • 38 Executive B. mHealth: use of appropriate digital technologies for public health: report by the Director-General. Geneva: World Health Organization; ; Available at: https://apps.who.int/iris/bitstream/handle/10665/274134/B142_20-en.pdf?sequence=1&isAllowed=y. Accessed November 27, 2017
  • 39 Tamaresis JS, Irwin JC, Goldfien GA. et al. Molecular classification of endometriosis and disease stage using high-dimensional genomic data. Endocrinology 2014; 155 (12) 4986-4999
  • 40 Fourquet J, Zavala DE, Missmer S, Bracero N, Romaguera J, Flores I. Disparities in healthcare services in women with endometriosis with public vs private health insurance. Am J Obstet Gynecol 2019; 221 (06) 623.e1-623.e11
  • 41 De Graaff AA, D'Hooghe TM, Dunselman GA, Dirksen CD, Hummelshoj L, Simoens S. WERF EndoCost Consortium. The significant effect of endometriosis on physical, mental and social wellbeing: results from an international cross-sectional survey. Hum Reprod 2013; 28 (10) 2677-2685
  • 42 Simoens S, Dunselman G, Dirksen C. et al. The burden of endometriosis: costs and quality of life of women with endometriosis and treated in referral centres. Hum Reprod 2012; 27 (05) 1292-1299
  • 43 Fourquet J, Gao X, Zavala D. et al. Patients' report on how endometriosis affects health, work, and daily life. Fertil Steril 2010; 93 (07) 2424-2428
  • 44 Rogers PA, D'Hooghe TM, Fazleabas A. et al. Priorities for endometriosis research: recommendations from an international consensus workshop. Reprod Sci 2009; 16 (04) 335-346
  • 45 Schliep KC, Mumford SL, Peterson CM. et al. Pain typology and incident endometriosis. Hum Reprod 2015; 30 (10) 2427-2438
  • 46 Soliman AM, Coyne KS, Gries KS, Castelli-Haley J, Snabes MC, Surrey ES. The effect of endometriosis symptoms on absenteeism and presenteeism in the workplace and at home. J Manag Care Spec Pharm 2017; 23 (07) 745-754
  • 47 Whiteman MK, Hillis SD, Jamieson DJ. et al. Inpatient hysterectomy surveillance in the United States, 2000-2004. Am J Obstet Gynecol 2008; 198 (01) 34.e1-34.e7
  • 48 Health, NIo, Estimates of funding for various research, condition, and disease categories (RCDC). . Available at: https://report.nih.gov/categorical_spending.aspx. Published 2019. Accessed March 15, 2020
  • 49 Garry R. Is insulin resistance an essential component of PCOS?: the endometriosis syndromes: a clinical classification in the presence of aetiological confusion and therapeutic anarchy. Hum Reprod 2004; 19 (04) 760-768
  • 50 Nnoaham KE, Hummelshoj L, Webster P. World Endometriosis Research Foundation Global Study of Women's Health consortium. et al; Impact of endometriosis on quality of life and work productivity: a multicenter study across ten countries. Fertil Steril 2011; 96 (02) 366-373
  • 51 Farland LV, Horne AW. Disparity in endometriosis diagnoses between racial/ethnic groups. BJOG 2019; 126 (09) 1115-1116
  • 52 Fourquet J, Báez L, Figueroa M, Iriarte RI, Flores I. Quantification of the impact of endometriosis symptoms on health-related quality of life and work productivity. Fertil Steril 2011; 96 (01) 107-112
  • 53 Chen EH, Shofer FS, Dean AJ. et al. Gender disparity in analgesic treatment of emergency department patients with acute abdominal pain. Acad Emerg Med 2008; 15 (05) 414-418
  • 54 Leresche L. Defining gender disparities in pain management. Clin Orthop Relat Res 2011; 469 (07) 1871-1877
  • 55 Ghiasi M, Kulkarni MT, Missmer SA. Is endometriosis more common and more severe than it was 30 years ago?. J Minim Invasive Gynecol 2020; 27 (02) 452-461
  • 56 Budrionis A, Bellika JG. The learning healthcare system: where are we now? A systematic review. J Biomed Inform 2016; 64: 87-92
  • 57 Galvin HK, Petersen C, Subbian V, Solomonides A. Patients as agents in behavioral health research and service provision: recommendations to support the learning health system. Appl Clin Inform 2019; 10 (05) 841-848
  • 58 McKillop M, Mamykina L, Elhadad N. Designing in the dark: eliciting self-tracking dimensions for understanding enigmatic disease. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Available at: http://people.dbmi.columbia.edu/noemie/papers/18chi.pdf. Accessed 2018
  • 59 McKillop M, Voigt N, Schnall R, Elhadad N. Exploring self-tracking as a participatory research activity among women with endometriosis. J Particip Med 2016; 8: e17
  • 60 Ensari I, Elhadad N. mHealth For Research: Participatory Research Applications to Gain Disease Insights. In Digital Health: Mobile and Wearable Devices for Participatory Health Applications. Eds. Syed-Abdul S, Zhu X, Fernandez-Luque L. Elsevier, 2020. . Forthcoming. ISBN: 978-0128200773
  • 61 Friedman C, Rubin J, Brown J. et al. Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. J Am Med Inform Assoc 2015; 22 (01) 43-50
  • 62 Hripcsak G, Duke JD, Shah NH. et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 2015; 216: 574-578
  • 63 Hripcsak G, Albers DJ. Correlating electronic health record concepts with healthcare process events. J Am Med Inform Assoc 2013; 20 (e2): e311-e318
  • 64 United States Census Bureau, QuickFacts. New York City, New York: United States Census Bureau. . Available at: https://data.census.gov/cedsci/all?q=new%20york%20city&g=1600000US3651000&hidePreview=false&tid=ACSDP1Y2018.DP05&vintage=2018. Accessed 2020
  • 65 Ta CN, Dumontier M, Hripcsak G, Tatonetti NP, Weng C. Columbia Open Health Data, clinical concept prevalence and co-occurrence from electronic health records. Sci Data 2018; 5: 180273-180273
  • 66 Awad E, Ahmed HAH, Yousef A, Abbas R. Efficacy of exercise on pelvic pain and posture associated with endometriosis: within subject design. J Phys Ther Sci 2017; 29 (12) 2112-2115
  • 67 The Observational Medical Outcomes Partnership (OMOP) Common Data Model. . Available at: https://ohdsi.github.io/CommonDataModel/background.html. Accessed 2020
  • 68 International Health Terminology Standards Development Organization, SNOMED CT Worldwide. . Available at: http://www.ihtsdo.org/snomed-ct/snomed-ct-worldwide. Accessed 2016
  • 69 Zini EM, Lanzola G, Bossi P, Quaglini S. An environment for guideline-based decision support systems for outpatients monitoring. Methods Inf Med 2017; 56 (04) 283-293
  • 70 Ramakrishnan N, Hanauer D, Keller B. Mining electronic health records. Computer 2010; (10) 77-81
  • 71 McKillop MM. Phenotyping endometriosis from observational health data. Columbia University. . Available at: https://academiccommons.columbia.edu/doi/10.7916/d8-1est-dh56. Accessed March 18, 2019
  • 72 Melzack R. The McGill Pain Questionnaire: major properties and scoring methods. Pain 1975; 1 (03) 277-299
  • 73 Simborg DW, Starfield BH, Horn SD, Yourtee SA. Information factors affecting problem follow-up in ambulatory care. Med Care 1976; 14 (10) 848-856
  • 74 Bartley EJ, Robinson ME, Staud R. Pain and fatigue variability patterns distinguish subgroups of fibromyalgia patients. J Pain 2018; 19 (04) 372-381
  • 75 Salthouse TA. Implications of within-person variability in cognitive and neuropsychological functioning for the interpretation of change. Neuropsychology 2007; 21 (04) 401-411
  • 76 Urteaga I, McKillop M, Elhadad N. Learning endometriosis phenotypes from patient-generated data. NPJ Digit Med 2020; 3: 88
  • 77 Wilson EB. Probable inference, the law of succession, and statistical inference. J Am Stat Assoc 1927; 22 (158) 209-212
  • 78 Newcombe RG. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat Med 1998; 17 (08) 873-890
  • 79 Harris RE, Williams DA, McLean SA. et al. Characterization and consequences of pain variability in individuals with fibromyalgia. Arthritis Rheum 2005; 52 (11) 3670-3674
  • 80 Stoffel MA, Nakagawa S, Schielzeth H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed‐effects models. Methods Ecol Evol 2017; 8 (11) 1639-1644
  • 81 Hox JJ, Moerbeek M, Van de Schoot R. Multilevel Analysis: Techniques and Applications. Routledge. 2010
  • 82 Nakagawa S, Schielzeth H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol Rev Camb Philos Soc 2010; 85 (04) 935-956
  • 83 Huang C-W, Lu R, Iqbal U. et al. A richly interactive exploratory data analysis and visualization tool using electronic medical records. BMC Med Inform Decis Mak 2015; 15: 92-92
  • 84 Bougie O, Healey J, Singh SS. Behind the times: revisiting endometriosis and race. Am J Obstet Gynecol 2019; 221 (01) 35.e1-35.e5
  • 85 Seracchioli R, Mabrouk M, Guerrini M. et al. Dyschezia and posterior deep infiltrating endometriosis: analysis of 360 cases. J Minim Invasive Gynecol 2008; 15 (06) 695-699
  • 86 Cagle J, Bunting M. Patient reluctance to discuss pain: understanding stoicism, stigma, and other contributing factors. J Soc Work End Life Palliat Care 2017; 13 (01) 27-43
  • 87 Norouzinia R, Aghabarari M, Shiri M, Karimi M, Samami E. Samami EJGjohs. Communication barriers perceived by nurses and patients. Glob J Health Sci 2015; 8 (06) 65-74
  • 88 Hoffmann DE, Tarzian AJ. The girl who cried pain: a bias against women in the treatment of pain. J Law Med Ethics 2001; 29 (01) 13-27
  • 89 As-Sanie S, Black R, Giudice LC. et al. Assessing research gaps and unmet needs in endometriosis. Am J Obstet Gynecol 2019; 221 (02) 86-94
  • 90 Weiskopf NG, Cohen AM, Hannan J, Jarmon T, Dorr DA. Towards augmenting structured EHR data: a comparison of manual chart review and patient self-report. AMIA Annu Symp Proc 2020; 2019: 903-912
  • 91 Farrar JT, Troxel AB, Haynes K. et al. Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: an ACTTION study. Pain 2014; 155 (08) 1622-1631
  • 92 Carvalho E, Bettger JP, Goode AP. Insurance coverage, costs, and barriers to care for outpatient musculoskeletal therapy and rehabilitation services. N C Med J 2017; 78 (05) 312-314
  • 93 Genes N, Violante S, Cetrangol C, Rogers L, Schadt EE, Chan YY. From smartphone to EHR: a case report on integrating patient-generated health data. NPJ Digit Med 2018; 1 (01) 23
  • 94 Alexander S. mHealth Technologies for the Self-management of Diabetes in the Older Population. ACM SIGACCESS Accessibility and Computing 2015; (111) 14-18
  • 95 Steinhubl SR, Muse ED, Topol EJ. The emerging field of mobile health. Sci Transl Med 2015; 7 (283) 283rv3
  • 96 Faurholt-Jepsen M, Munkholm K, Frost M, Bardram JE, Kessing LV. Electronic self-monitoring of mood using IT platforms in adult patients with bipolar disorder: a systematic review of the validity and evidence. BMC Psychiatry 2016; 16 (01) 7
  • 97 Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med 2017; 7 (02) 254-267
  • 98 Honoré P, Menning PM, Rogers SD, Nichols ML, Mantyh PW. Neurochemical plasticity in persistent inflammatory pain. In: Progress in brain research. Vol 129. Elsevier; 2000: 357-363
  • 99 Sinatra R. Causes and consequences of inadequate management of acute pain. Pain Med 2010; 11 (12) 1859-1871
  • 100 Tasmuth T, Kataja M, Blomqvist C, von Smitten K, Kalso E. Treatment-related factors predisposing to chronic pain in patients with breast cancer--a multivariate approach. Acta Oncol 1997; 36 (06) 625-630
  • 101 Poleshuck EL, Katz J, Andrus CH. et al. Risk factors for chronic pain following breast cancer surgery: a prospective study. J Pain 2006; 7 (09) 626-634
  • 102 Fremont A, Lurie N. Eliminating health disparities: measurement and data needs. Appendix D, The role of racial and ethnic data collection in eliminating disparities in health care. Washington (DC): National Academies Press (US); 2004
  • 103 Birkhead GS, Klompas M, Shah NR. Uses of electronic health records for public health surveillance to advance public health. Annu Rev Public Health 2015; 36 (01) 345-359
  • 104 Douglas MD, Dawes DE, Holden KB, Mack D. Missed policy opportunities to advance health equity by recording demographic data in electronic health records. Am J Public Health 2015; 105 (Suppl. 03) S380-S388
  • 105 Polubriaginof FCG, Ryan P, Salmasian H. et al. Challenges with quality of race and ethnicity data in observational databases. J Am Med Inform Assoc 2019; 26 (8-9): 730-736
  • 106 Kiepek W, Sengstack PP. An evaluation of system end-user support during implementation of an electronic health record using the model for improvement framework. Appl Clin Inform 2019; 10 (05) 964-971
  • 107 Thyvalikakath TP, Duncan WD, Siddiqui Z. National Dental PBRN Collaborative Group. et al; Leveraging electronic dental record data for clinical research in the national dental PBRN practices. Appl Clin Inform 2020; 11 (02) 305-314
  • 108 Charmel PA, Frampton SB. Building the business case for patient-centered care. Healthc Financ Manage 2008; 62 (03) 80-85
  • 109 Chiauzzi E, Rodarte C, DasMahapatra P. Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med 2015; 13 (01) 77
  • 110 Korotitsch WJ, Nelson-Gray RO. An overview of self-monitoring research in assessment and treatment. Psychol Assess 1999; 11 (04) 415
  • 111 Chase HS, Radhakrishnan J, Shirazian S, Rao MK, Vawdrey DK. Under-documentation of chronic kidney disease in the electronic health record in outpatients. J Am Med Inform Assoc 2010; 17 (05) 588-594